1. Field of the Invention
The present invention relates to a vehicle distance data processor for matching objects having the same time series when measuring the distance of a vehicle to another vehicle or obstacle ahead, and a relative velocity therebetween.
2. Description of the Related Art
In a vehicle-to-vehicle distance alarm device which generates an alarm when a vehicle having the alarm device mounted thereon (hereinafter referred to as a subject vehicle) comes within a dangerous distance to another vehicle ahead (hereinafter referred to as a preceding vehicle), the velocity of the subject vehicle relative to the preceding vehicle must also be measured in addition to the distance therebetween. However, when a distance measuring means similar to a scanning-type distance measuring device having a plurality of distance measurement areas is used with the above-described vehicle-to-vehicle distance alarm device, a problem arises as to which successive distance data to choose among the plurality of distance data, measured at a fixed time interval, to obtain the relative velocity. In order to overcome such a problem, there has been conventionally proposed a vehicle distance data processor such as that disclosed, for example, in Japanese Patent Publication No. 3-6472, in which a difference between the previous and current distances is determined for each direction of detection to obtain the relative velocity. In such a prior art device, however, when an object to be detected is moving sideways so that it moves into an area of detection just next to the current one, it might not be possible to continuously provide correct relative velocity data because the sidewise movement cannot be detected.
Accordingly, if the successively detected points are correlated or matched in time series to each other (i.e., currently detected points are correlated or matched to the lastly detected points), the relative velocity can be computed even for objects which have moved sideways. To this end, although not known to public, a vehicle distance data processor having, for example, a structure as that illustrated in FIG. 13 may be considered.
As apparent from FIG. 13, the vehicle distance data processor includes a distance measuring element 1 for measuring the distance from a subject vehicle to another vehicle or obstacle ahead and generating a corresponding output; a distance data input element 2 for inputting the output of the distance measuring element 1 into a computer 10; a distance data converting element 3 for converting distance data input by the distance data input element 2 to coordinate data in the form of two-dimensional coordinate data; a coordinate data predicting element 4 for predicting coordinate data to be obtained from the current measurement (to be described later) based on the previous coordinate data which is obtained through conversion of the previously measured distance data by means of the distance data converting element 3; a correlating element 5 for correlating the currently obtained coordinate data with the previously obtained coordinate data based on the coordinate data predicted by the coordinate data predicting element 4; a relative velocity computing element 6 for computing relative velocity data related to each detection point based on the previously and currently measured coordinate data correlated or matched by the correlating element 5; a candidate or predicted vehicle data computing element 7 for obtaining candidate or predicted vehicle data from the currently obtained coordinate data or relative velocities detected; and a display 8 for displaying this result.
FIG. 14 is a flowchart illustrating processing of data carried out by the above-described vehicle distance data processor. The flowchart of such data processing will be described below, in Step 101, distance data obtained, for example, by the distance measuring element 1 similar to a scanning-type distance meter using a laser beam is input via distance data input element 2 to the computer 10. In Step 102, the input data is converted to corresponding two-dimensional coordinate data by the distance data converting element 3. Then in Step 103, the current coordinates of each of the detected points are predicted by the coordinate data predicting element 4 based on the x and y coordinates of the past detected points and relative velocity vector data. In Step 104, the correlating element 5 compares the coordinate data predicted in Step 103 with the currently obtained coordinate data for each detected point, so that the predicted coordinate data is correlated or matched to the currently obtained coordinate data if they are determined to be identical, i.e., if they satisfy the following Relationships (1) and (2); EQU .vertline.Xi-Xj.vertline..ltoreq.X (1) EQU .vertline.Yi-Yj.vertline..ltoreq.Y (2)
where i represents a coordinate of a currently detected point, and j represents a coordinate of a predicted detection point.
In Step 105, based on changes in the previous and current coordinates which were correlated or matched in Step 104, a relative velocity vector is obtained by the relative velocity computing element 6. Then, in Step 106, the detection points or points to be detected are computed to obtain predicted vehicle data by the predicted vehicle data computing element 7 to provide the coordinate data and a relative velocity vector thereof. The display 8 generates an alarm based on the coordinate data and the relative velocity vector data for the predicted vehicle data obtained in this manner.
However, in the above-described device, problems occur when the coordinate data and relative velocity vector data, required to obtain the predicted position, are not accurate. For example, when x and y coordinates in Formulas (1) and (2) above are made small, corresponding coordinates cannot be found so that successive variations in distance cannot be obtained. That is, a new candidate vehicle data appears each time the distance is measured. On the other hand, when x and y coordinates are made large, a plurality of corresponding coordinates appear so that an error may occur depending on the way of correlation of the coordinate data. Thus, coordinates and relative velocity vector data different from those of the actual candidate vehicle data result, generating a false alarm.